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Vol 18, No 1 (2024)

The International Journal of Design is a peer-reviewed, open-access journal devoted to publishing research papers in all fields of design, including industrial design, visual communication design, interface design, animation and game design, architectural design, urban design, and other design related fields. It aims to provide an international forum for the exchange of ideas and findings from researchers across different cultures and encourages research on the impact of cultural factors on design theory and practice. It also seeks to promote the transfer of knowledge between professionals in academia and industry by emphasizing research in which results are of interest or applicable to design practices.

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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 3, 2023

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

Data type required for research
Research resources
Participants required for research
Hypothesis based upon research question(s)
Data analysis methodologies
Variables (Independent, dependent, and confounding)
The location and timescale for conducting the data
The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and interpretation techniques. A strong research design reflects a strong dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a dissertation analyzing different situations or cases will have more than one research design.

For example:

Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
Quantitative research is good for the statistical part of the project, but it may not provide an in-depth understanding of the topic .
Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

Do you have enough time to gather data and complete the write-up?
Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
Do you have in-depth knowledge about the different statistical analysis and data collection techniques to address the research questions or test the hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative vs. quantitative data.

Also, see; Research methods, design, and analysis .

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Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results and conclusion .

Read our guidelines to write a research proposal if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The research methodology or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

Organizing Your Social Sciences Research Paper

Types of Research Designs
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Applying Critical Thinking
Choosing a Title
Making an Outline
Paragraph Development
Research Process Video Series
Executive Summary
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
Quantitative Methods
Insiderness
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Generative AI and Writing
USC Libraries Tutorials and Other Guides
Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
Review and synthesize previously published literature associated with the research problem,
Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

This is a collaborative and adaptive research design that lends itself to use in work or community situations.
Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
Action research studies often have direct and obvious relevance to improving practice and advocating for change.
There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
Personal over-involvement of the researcher may bias research results.
The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA: Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
Design can extend experience or add strength to what is already known through previous research.
Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
The design can provide detailed descriptions of specific and rare cases.
A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
Design does not facilitate assessment of cause and effect relationships.
Vital information may be missing, making the case hard to interpret.
The case may not be representative or typical of the larger problem being investigated.
If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
Replication is possible.
There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
Either original data or secondary data can be used in this design.
In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
Studies cannot be utilized to establish cause and effect relationships.
This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
If the limitations are understood, they can be a useful tool in developing a more focused study.
Descriptive studies can yield rich data that lead to important recommendations in practice.
Appoach collects a large amount of data for detailed analysis.
The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
Approach provides the highest level of evidence for single studies.
The design is artificial, and results may not generalize well to the real world.
The artificial settings of experiments may alter the behaviors or responses of participants.
Experimental designs can be costly if special equipment or facilities are needed.
Some research problems cannot be studied using an experiment because of ethical or technical reasons.
Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

Familiarity with basic details, settings, and concerns.
Well grounded picture of the situation being developed.
Generation of new ideas and assumptions.
Development of tentative theories or hypotheses.
Determination about whether a study is feasible in the future.
Issues get refined for more systematic investigation and formulation of new research questions.
Direction for future research and techniques get developed.
Design is a useful approach for gaining background information on a particular topic.
Exploratory research is flexible and can address research questions of all types (what, why, how).
Provides an opportunity to define new terms and clarify existing concepts.
Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field [i.e., the act of triangulating the data].
Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

The historical research design is unobtrusive; the act of research does not affect the results of the study.
The historical approach is well suited for trend analysis.
Historical records can add important contextual background required to more fully understand and interpret a research problem.
There is often no possibility of researcher-subject interaction that could affect the findings.
Historical sources can be used over and over to study different research problems or to replicate a previous study.
The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
Interpreting historical sources can be very time consuming.
The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58; Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
The data collection method may change over time.
Maintaining the integrity of the original sample can be difficult over an extended period of time.
It can be difficult to show more than one variable at a time.
This design often needs qualitative research data to explain fluctuations in the results.
A longitudinal research design assumes present trends will continue unchanged.
It can take a long period of time to gather results.
There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
A well-reasoned and well-documented justification for identification and selection of the studies;
Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
Justification of the techniques used to evaluate the studies.
Can be an effective strategy for determining gaps in the literature.
Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
Can be used to generate new hypotheses or highlight research problems for future studies.
Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
A large sample size can yield reliable, but not necessarily valid, results.
A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
The strengths of one method can be used to overcome the inherent weaknesses of another method.
Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
The researcher is able to collect in-depth information about a particular behavior.
Can reveal interrelationships among multifaceted dimensions of group interactions.
You can generalize your results to real life situations.
Observational research is useful for discovering what variables may be important before applying other methods like experiments.
Observation research designs account for the complexity of group behaviors.
Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
There can be problems with bias as the researcher may only "see what they want to see."
There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
Sources or subjects may not all be equally credible.
Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
Can provide a basis for applying ethical decision-making to practice.
Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
Brings clarity to general guiding practices and principles of an individual or group.
Philosophy informs methodology.
Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
Limited application to specific research problems [answering the "So What?" question in social science research].
Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
There are limitations in the use of metaphor as a vehicle of philosophical analysis.
There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

The researcher has a limitless option when it comes to sample size and the sampling schedule.
Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
This is a useful design for exploratory studies.
There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods . David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research." Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall aims and approach
The type of research design you’ll use
Your sampling methods or criteria for selecting subjects
Your data collection methods
The procedures you’ll follow to collect data
Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

How much time do you have to collect data and write up the research?
Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?
What inclusion and exclusion criteria will you use to identify eligible participants?
How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)
The central tendency of the data (e.g., the mean to describe the average score)
The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

Make estimates about the population based on your sample data.
Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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User Interface Design and UX Design: 80+ Important Research Papers Covering Peer-Reviewed and Informal Studies

Charles Mauro CHFP

Important peer-reviewed and informally published recent research on user interface design and user experience (UX) design.

For the benefit of clients and colleagues we have culled a list of approximately 70 curated recent research publications dealing with user interface design, UX design and e-commerce optimization.

In our opinion these publications represent some of the best formal research thinking on UI and UX design. These papers are also among the most widely downloaded and cited formal research on UI / UX design. We have referenced many of these studies in our work at MauroNewMedia.

Pay walls: As you will note in reviewing the following links and abstracts, most of the serious research on UI / UX design and optimization is located behind pay walls controlled by major publishers. However, in the end, good data is well worth the investment. Many links and other cited references are, of course, free.

Important disclaimer: We do not receive any form of compensation for citing any of the following content. Either Charles L Mauro CHFP or Paul Thurman MBA has personally reviewed all papers and links in this list. Some of these references were utilized in the recent NYTECH UX talk given by Paul Thurman MBA titled: Critical New UX Design Optimization Research

In addition to historical research papers, we frequently receive requests from colleagues, clients and journalists for recommended reading lists on topics covering our expertise in UX design, usability research and human factors engineering. These requests prompted us to pull from our research library (yes, we still have real books) 30+ books which our professional staff felt should be considered primary conceptual literature for anyone well-read in the theory and practice of UX design and research. Please follow the for PulseUX’s compilation of the 30+ Best UX Design and Research Books of All Time

Title: The influence of hedonic and utilitarian motivations on user engagement: The case of online shopping experiences

Abstract User experience seeks to promote rich, engaging interactions between users and systems. In order for this experience to unfold, the user must be motivated to initiate an interaction with the technology. This study explored hedonic and utilitarian motivations in the context of user engagement with online shopping. Factor analysis was performed to identify a parsimonious set of factors from the Hedonic and Utilitarian Shopping Motivation Scale and the User Engagement Scale based on responses from 802 shoppers. Multiple linear regression was used to test hypotheses with hedonic and utilitarian motivations (Idea, Social, Adventure/Gratification, Value and Achievement Shopping) and attributes of user engagement (Aesthetics, Focused Attention, Perceived Usability, and Endurability). Results demonstrate the salience of Adventure/Gratification Shopping and Achievement Shopping Motivations to specific variables of user engagement in the e-commerce environment and provide considerations for the inclusion of different types of motivation into models of engaging user experiences. Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: New Support for Marketing Analytics

Abstract Consumer surveys and myriad other forms of research have long been the grist for marketing decisions at large companies. But many firms have been reluctant to embrace the high-tech approach to data gathering and number crunching that falls under the rubric of marketing analytics, which uses advanced techniques to transform the tracking of promotional efforts, customer preferences, and industry developments into sophisticated branding and advertising campaigns. Fueled in part by Tom Peters and Robert Waterman’s seminal 1982 book In Search of Excellence , which coined the phrase “paralysis through analysis,” skepticism about the approach remains widespread, even in the face of a number of positive research results over the years. This new study, involving Fortune 1000 companies, offers yet more ammunition for supporters of marketing analytics. Abstract Copyright © 2013 Booz & Company Inc. All rights reserved.

Title: Video game values: Human-computer interaction and games

Abstract Current human–computer interaction (HCI) research into video games rarely considers how they are different from other forms of software. This leads to research that, while useful concerning standard issues of interface design, does not address the nature of video games as games specifically. Unlike most software, video games are not made to support external, user-defined tasks, but instead define their own activities for players to engage in. We argue that video games contain systems of values which players perceive and adopt, and which shape the play of the game. A focus on video game values promotes a holistic view of video games as software, media, and as games specifically, which leads to a genuine video game HCI. Abstract Copyright © 2006 Elsevier B.V. All rights reserved.

Title: When fingers do the talking: a study of text messaging

Abstract SMS or text messaging is an area of growth in the communications field. The studies described below consisted of a questionnaire and a diary study. The questionnaire was designed to examine texting activities in 565 users of the mobile phone. The diary study was carried out by 24 subjects over a period of 2 weeks. The findings suggest that text messaging is being used by a wide range of people for all kinds of activities and that for some people it is the preferred means of communication. These studies should prove interesting for those examining the use and impact of SMS. Abstract Copyright © 2004 Elsevier B.V. All rights reserved.

Title: Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective

Abstract As mobile technology has developed, mobile banking has become accepted as part of daily life. Although many studies have been conducted to assess users’ satisfaction with mobile applications, none has focused on the ways in which the three quality factors associated with mobile banking – system quality, information quality and interface design quality – affect consumers’ trust and satisfaction. Our proposed research model, based on DeLone and McLean’s model, assesses how these three external quality factors can impact satisfaction and trust. We collected 276 valid questionnaires from mobile banking customers, then analyzed them using structural equation modeling. Our results show that system quality and information quality significantly influence customers’ trust and satisfaction, and that interface design quality does not. We present herein implications and suggestions for further research. Abstract Copyright © 2009 Elsevier B.V. All rights reserved.

Title: What is beautiful is usable

Abstract An experiment was conducted to test the relationships between users’ perceptions of a computerized system’s beauty and usability. The experiment used a computerized application as a surrogate for an Automated Teller Machine (ATM). Perceptions were elicited before and after the participants used the system. Pre-experimental measures indicate strong correlations between system’s perceived aesthetics and perceived usability. Post-experimental measures indicated that the strong correlation remained intact. A multivariate analysis of covariance revealed that the degree of system’s aesthetics affected the post-use perceptions of both aesthetics and usability, whereas the degree of actual usability had no such effect. The results resemble those found by social psychologists regarding the effect of physical attractiveness on the valuation of other personality attributes. The findings stress the importance of studying the aesthetic aspect of human–computer interaction (HCI) design and its relationships to other design dimensions. Abstract Copyright © 2000 Elsevier Science B.V. All rights reserved.

Title: UX Curve: A method for evaluating long-term user experience

Abstract The goal of user experience design in industry is to improve customer satisfaction and loyalty through the utility, ease of use, and pleasure provided in the interaction with a product. So far, user experience studies have mostly focused on short-term evaluations and consequently on aspects relating to the initial adoption of new product designs. Nevertheless, the relationship between the user and the product evolves over long periods of time and the relevance of prolonged use for market success has been recently highlighted. In this paper, we argue for the cost-effective elicitation of longitudinal user experience data. We propose a method called the “UX Curve” which aims at assisting users in retrospectively reporting how and why their experience with a product has changed over time. The usefulness of the UX Curve method was assessed in a qualitative study with 20 mobile phone users. In particular, we investigated how users’ specific memories of their experiences with their mobile phones guide their behavior and their willingness to recommend the product to others. The results suggest that the UX Curve method enables users and researchers to determine the quality of long-term user experience and the influences that improve user experience over time or cause it to deteriorate. The method provided rich qualitative data and we found that an improving trend of perceived attractiveness of mobile phones was related to user satisfaction and willingness to recommend their phone to friends. This highlights that sustaining perceived attractiveness can be a differentiating factor in the user acceptance of personal interactive products such as mobile phones. The study suggests that the proposed method can be used as a straightforward tool for understanding the reasons why user experience improves or worsens in long-term product use and how these reasons relate to customer loyalty. Abstract Copyright 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Heuristic evaluation: Comparing ways of finding and reporting usability problems

Abstract Research on heuristic evaluation in recent years has focused on improving its effectiveness and efficiency with respect to user testing. The aim of this paper is to refine a research agenda for comparing and contrasting evaluation methods. To reach this goal, a framework is presented to evaluate the effectiveness of different types of support for structured usability problem reporting. This paper reports on an empirical study of this framework that compares two sets of heuristics, Nielsen’s heuristics and the cognitive principles of Gerhardt-Powals, and two media of reporting a usability problem, i.e. either using a web tool or paper. The study found that there were no significant differences between any of the four groups in effectiveness, efficiency and inter-evaluator reliability. A more significant contribution of this research is that the framework used for the experiments proved successful and should be reusable by other researchers because of its thorough structure. Abstract Copyright © 2006 Elsevier B.V. All rights reserved.

Title: Socio-technical systems: From design methods to systems engineering

Abstract It is widely acknowledged that adopting a socio-technical approach to system development leads to systems that are more acceptable to end users and deliver better value to stakeholders. Despite this, such approaches are not widely practised. We analyse the reasons for this, highlighting some of the problems with the better known socio-technical design methods. Based on this analysis we propose a new pragmatic framework for socio-technical systems engineering (STSE) which builds on the (largely independent) research of groups investigating work design, information systems, computer-supported cooperative work, and cognitive systems engineering. STSE bridges the traditional gap between organisational change and system development using two main types of activity: sensitisation and awareness; and constructive engagement. From the framework, we identify an initial set of interdisciplinary research problems that address how to apply socio-technical approaches in a cost-effective way, and how to facilitate the integration of STSE with existing systems and software engineering approaches. Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: Five reasons for scenario-based design

Abstract Scenarios of human–computer interaction help us to understand and to create computer systems and applications as artifacts of human activity—as things to learn from, as tools to use in one’s work, as media for interacting with other people. Scenario-based design of information technology addresses five technical challenges: scenarios evoke reflection in the content of design work, helping developers coordinate design action and reflection. Scenarios are at once concrete and flexible, helping developers manage the fluidity of design situations. Scenarios afford multiple views of an interaction, diverse kinds and amounts of detailing, helping developers manage the many consequences entailed by any given design move. Scenarios can also be abstracted and categorized, helping designers to recognize, capture and reuse generalizations and to address the challenge that technical knowledge often lags the needs of technical design. Finally, scenarios promote work-oriented communication among stakeholders, helping to make design activities more accessible to the great variety of expertise that can contribute to design, and addressing the challenge that external constraints designers and clients face often distract attention from the needs and concerns of the people who will use the technology. Abstract Copyright © 2000 Elsevier Science B.V. All rights reserved.

Title: Needs, affect, and interactive products – Facets of user experience

Abstract Subsumed under the umbrella of User Experience (UX), practitioners and academics of Human–Computer Interaction look for ways to broaden their understanding of what constitutes “pleasurable experiences” with technology. The present study considered the fulfilment of universal psychological needs, such as competence, relatedness, popularity, stimulation, meaning, security, or autonomy, to be the major source of positive experience with interactive technologies. To explore this, we collected over 500 positive experiences with interactive products (e.g., mobile phones, computers). As expected, we found a clear relationship between need fulfilment and positive affect, with stimulation, relatedness, competence and popularity being especially salient needs. Experiences could be further categorized by the primary need they fulfil, with apparent qualitative differences among some of the categories in terms of the emotions involved. Need fulfilment was clearly linked to hedonic quality perceptions, but not as strongly to pragmatic quality (i.e., perceived usability), which supports the notion of hedonic quality as “motivator” and pragmatic quality as “hygiene factor.” Whether hedonic quality ratings reflected need fulfilment depended on the belief that the product was responsible for the experience (i.e., attribution). Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: The role of social presence in establishing loyalty in e-Service environments

Abstract Compared to offline shopping, the online shopping experience may be viewed as lacking human warmth and sociability as it is more impersonal, anonymous, automated and generally devoid of face-to-face interactions. Thus, understanding how to create customer loyalty in online environments (e-Loyalty) is a complex process. In this paper a model for e-Loyalty is proposed and used to examine how varied conditions of social presence in a B2C e-Services context influence e-Loyalty and its antecedents of perceived usefulness, trust and enjoyment. This model is examined through an empirical study involving 185 subjects using structural equation modeling techniques. Further analysis is conducted to reveal gender differences concerning hedonic elements in the model on e-Loyalty. Abstract Copyright © 2006 Elsevier B.V. All rights reserved.

Title: A framework for evaluating the usability of mobile phones based on multi-level, hierarchical model of usability factors

Abstract As a mobile phone has various advanced functionalities or features, usability issues are increasingly challenging. Due to the particular characteristics of a mobile phone, typical usability evaluation methods and heuristics, most of which are relevant to a software system, might not effectively be applied to a mobile phone. Another point to consider is that usability evaluation activities should help designers find usability problems easily and produce better design solutions. To support usability practitioners of the mobile phone industry, we propose a framework for evaluating the usability of a mobile phone, based on a multi-level, hierarchical model of usability factors, in an analytic way. The model was developed on the basis of a set of collected usability problems and our previous study on a conceptual framework for identifying usability impact factors. It has multi-abstraction levels, each of which considers the usability of a mobile phone from a particular perspective. As there are goal-means relationships between adjacent levels, a range of usability issues can be interpreted in a holistic as well as diagnostic way. Another advantage is that it supports two different types of evaluation approaches: task-based and interface-based. To support both evaluation approaches, we developed four sets of checklists, each of which is concerned, respectively, with task-based evaluation and three different interface types: Logical User Interface (LUI), Physical User Interface (PUI) and Graphical User Interface (GUI). The proposed framework specifies an approach to quantifying usability so that several usability aspects are collectively measured to give a single score with the use of the checklists. A small case study was conducted in order to examine the applicability of the framework and to identify the aspects of the framework to be improved. It showed that it could be a useful tool for evaluating the usability of a mobile phone. Based on the case study, we improved the framework in order that usability practitioners can use it more easily and consistently. Abstract Copyright © 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Understanding the most satisfying and unsatisfying user experiences: Emotions, psychological needs, and context

Abstract The aim of this research was to study the structure of the most satisfying and unsatisfying user experiences in terms of experienced emotions, psychological needs, and contextual factors. 45 university students wrote descriptions of their most satisfying and unsatisfying recent user experiences and analyzed those experiences using the Positive and Negative Affect Schedule (PANAS) method for experienced emotions, a questionnaire probing the salience of 10 psychological needs, and a self-made set of rating scales for analyzing context. The results suggested that it was possible to capture variations in user experiences in terms of experienced emotions, fulfillment of psychological needs, and context effectively by using psychometric rating scales. The results for emotional experiences showed significant differences in 16 out of 20 PANAS emotions between the most satisfying and unsatisfying experiences. The results for psychological needs indicated that feelings of autonomy and competence emerged as highly salient in the most satisfying experiences and missing in the unsatisfying experiences. High self-esteem was also notably salient in the most satisfying experiences. The qualitative results indicated that most of the participants’ free-form qualitative descriptions, especially for the most unsatisfying user experiences, gave important information about the pragmatic aspects of the interaction, but often omitted information about hedonic and social aspects of user experience. Abstract Copyright © 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: The Usability Metric for User Experience

Abstract The Usability Metric for User Experience (UMUX) is a four-item Likert scale used for the subjective assessment of an application’s perceived usability. It is designed to provide results similar to those obtained with the 10-item System Usability Scale, and is organized around the ISO 9241-11 definition of usability. A pilot version was assembled from candidate items, which was then tested alongside the System Usability Scale during usability testing. It was shown that the two scales correlate well, are reliable, and both align on one underlying usability factor. In addition, the Usability Metric for User Experience is compact enough to serve as a usability module in a broader user experience metric. Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: User acceptance of mobile Internet: Implication for convergence technologies

Abstract Using the Technology Acceptance Model as a conceptual framework and a method of structural equation modeling, this study analyzes the consumer attitude toward Wi-Bro drawing data from 515 consumers. Individuals’ responses to questions about whether they use/accept Wi-Bro were collected and combined with various factors modified from the Technology Acceptance Model.

The result of this study show that users’ perceptions are significantly associated with their motivation to use Wi-Bro. Specifically, perceived quality and perceived availability are found to have significant effect on users’ extrinsic and intrinsic motivation. These new factors are found to be Wi-Bro-specific factors, playing as enhancing factors to attitudes and intention. Abstract Copyright © 2007 Elsevier B.V. All rights reserved.

Title: Understanding purchasing behaviors in a virtual economy: Consumer behavior involving virtual currency in Web 2.0 communities

Abstract This study analyzes consumer purchasing behavior in Web 2.0, expanding the technology acceptance model (TAM), focusing on which variables influence the intention to transact with virtual currency. Individuals’ responses to questions about attitude and intention to transact in Web 2.0 were collected and analyzed with various factors modified from the TAM. The results of the proposed model show that subjective norm is a key behavioral antecedent to using virtual currency. In the extended model, the moderating effects of subjective norm on the relations among the variables were found to be significant. The new set of variables is virtual environment-specific, acting as factors enhancing attitudes and behavioral intentions in Web 2.0 transactions. Abstract Copyright © 2008 Elsevier B.V. All rights reserved.

Title: Fundamentals of physiological computing

Abstract This review paper is concerned with the development of physiological computing systems that employ real-time measures of psychophysiology to communicate the psychological state of the user to an adaptive system. It is argued that physiological computing has enormous potential to innovate human–computer interaction by extending the communication bandwidth to enable the development of ‘smart’ technology. This paper focuses on six fundamental issues for physiological computing systems through a review and synthesis of existing literature, these are (1) the complexity of the psychophysiological inference, (2) validating the psychophysiological inference, (3) representing the psychological state of the user, (4) designing explicit and implicit system interventions, (5) defining the biocybernetic loop that controls system adaptation, and (6) ethical implications. The paper concludes that physiological computing provides opportunities to innovate HCI but complex methodological/conceptual issues must be fully tackled during the research and development phase if this nascent technology is to achieve its potential. Abstract Copyright © 2008 Elsevier B.V. All rights reserved.

Title: Modelling user experience with web sites: Usability, hedonic value, beauty and goodness

Abstract Recent research into user experience has identified the need for a theoretical model to build cumulative knowledge in research addressing how the overall quality or ‘goodness’ of an interactive product is formed. An experiment tested and extended Hassenzahl’s model of aesthetic experience. The study used a 2 × 2 × (2) experimental design with three factors: principles of screen design, principles for organizing information on a web page and experience of using a web site. Dependent variables included hedonic perceptions and evaluations of a web site as well as measures of task performance, navigation behaviour and mental effort. Measures, except Beauty, were sensitive to manipulation of web design. Beauty was influenced by hedonic attributes (identification and stimulation), but Goodness by both hedonic and pragmatic (user-perceived usability) attributes as well as task performance and mental effort. Hedonic quality was more stable with experience of web-site use than pragmatic quality and Beauty was more stable than Goodness. Abstract Copyright © 2008 Elsevier B.V. All rights reserved.

Title: Sample Size In Usability Studies

Abstract Usability studies are a cornerstone activity for developing usable products. Their effectiveness depends on sample size, and determining sample sizehas been a research issue in usability engineering for the past 30 years. In 2010, Hwang and Salvendy reported a meta study on the effectiveness of usability evaluation, concluding that a sample size of 10±2 is sufficient for discovering 80% of usability problems (not five, as suggested earlier by Nielsen in 2000). Here, I show the Hwang and Salvendy study ignored fundamental mathematical properties of the problem, severely limiting the validity of the 10±2 rule, then look to reframe the issue of effectiveness and sample-size estimation to the practices and requirements commonly encountered in industrial-scale usability studies. Abstract Copyright © 2013 ACM, Inc. Title: An experimental study of learner perceptions of the interactivity of web-based instruction

Abstract An effectively designed interaction mechanism creates a shortcut for human–computer interaction. Most studies in this area have concluded that the higher the level of interactivity, the better, especially regarding interactive websites applied in the fields of business and education. Previous studies have also suggested that designs with a higher level of interactivity result in higher learner evaluations of websites. However, little research has examined learner perceptions as they interact with web-based instruction (WBI) systems in a situation with limited time. To assist learners in acquiring knowledge quickly, the interactivity design must make the web learning environment easier to use by reducing the complexity of the interface. The aim of the present study is to explore learner perceptions of three WBI systems with different interaction levels under time limitations. This study was therefore designed to provide a new framework to design systems with different degrees of interactivity, and to examine learners’ perceptions of these interaction elements. Three WBI systems were developed with different degrees of interactivity from high to low, and a between-subject experiment was conducted with 45 subjects. The results of the experiment indicate that a higher level of interactivity does not necessarily guarantee a higher perception of interactivity in a short-term learning situation. Therefore, the instructors must pay attention to modifying or selecting appropriate interactive elements that are more suitable for various learning stages. The findings provide insights for designers to adopt different degrees of interactivity in their designs that will best fulfill various learners’ needs. Abstract Copyright © 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Age differences in the perception of social presence in the use of 3D virtual world for social interaction

Abstract 3D virtual worlds are becoming increasingly popular as tool for social interaction, with the potential of augmenting the user’s perception of physical and social presence. Thus, this technology could be of great benefit to older people, providing home-bound older users with access to social, educational and recreational resources. However, so far there have been few studies looking into how older people engage with virtual worlds, as most research in this area focuses on younger users. In this study, an online experiment was conducted with 30 older and 30 younger users to investigate age differences in the perception of presence in the use of virtual worlds for social interaction. Overall, we found that factors such as navigation and prior experience with text messaging tools played a key role in older people’s perception of presence. Both physical and social presence was found to be linked to the quality of social interaction for users of both age groups. In addition, older people displayed proxemic behavior which was more similar to proxemic behavior in the physical world when compared to younger users. Abstract Copyright © 2012 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Human error and information systems failure: the case of the London ambulance service computer-aided despatch system project

Abstract Human error and systems failure have been two constructs that have become linked in many contexts. In this paper we particularly focus on the issue of failure in relation to that group of software systems known as information systems. We first review the extant theoretical and empirical work on this topic. Then we discuss one particular well-known case — that of the London ambulance service computer-aided despatch system (Lascad) project — and use it as a particularly cogent example of the features of information systems failure. We maintain that the tendency to analyse information systems failure solely from a technological standpoint is limiting, that the nature of information systems failure is multi-faceted, and hence cannot be adequately understood purely in terms of the immediate problems of systems construction. Our purpose is also to use the generic material on IS failure and the specific details of this particular case study to critique the issues of safety, criticality, human error and risk in relation to systems not currently well considered in relation to these areas. Abstract Copyright © 1999 Elsevier B.V. All rights reserved.

Title: Feminist HCI meets facebook: Performativity and social networking sites

Abstract In this paper, I reflect on a specific product of interaction design, social networking sites. The goals of this paper are twofold. One is to bring a feminist reflexivity, to HCI, drawing on the work of Judith Butler and her concepts of peformativity, citationality, and interpellation. Her approach is, I argue, highly relevant to issues of identity and self-representation on social networking sites; and to the co-constitution of the subject and technology. A critical, feminist HCI must ask how social media and other HCI institutions, practices, and discourses are part of the processes by which sociotechnical configurations are constructed. My second goal is to examine the implications of such an approach by applying it to social networking sites (SNSs) drawing the empirical research literature on SNSs, to show how SNS structures and policies help shape the subject and hide the contingency of subject categories. Abstract Copyright © 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing

Abstract Physiological computing represents a mode of human–computer interaction where the computer monitors, analyzes and responds to the user’s psychophysiological activity in real-time. Within the field, autonomic nervous system responses have been studied extensively since they can be measured quickly and unobtrusively. However, despite a vast body of literature available on the subject, there is still no universally accepted set of rules that would translate physiological data to psychological states. This paper surveys the work performed on data fusion and system adaptation using autonomic nervous system responses in psychophysiology and physiological computing during the last ten years. First, five prerequisites for data fusion are examined: psychological model selection, training set preparation, feature extraction, normalization and dimension reduction. Then, different methods for either classification or estimation of psychological states from the extracted features are presented and compared. Finally, implementations of system adaptation are reviewed: changing the system that the user is interacting with in response to cognitive or affective information inferred from autonomic nervous system responses. The paper is aimed primarily at psychologists and computer scientists who have already recorded autonomic nervous system responses and now need to create algorithms to determine the subject’s psychological state. Abstract Copyright © 2012 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Positive mood induction procedures for virtual environments designed for elderly people

Abstract Positive emotions have a significant influence on mental and physical health. Their role in the elderly’s wellbeing has been established in numerous studies. It is therefore worthwhile to explore ways in which elderly people can increase the number of positive experiences in their daily lives. This paper describes two Virtual Environments (VEs) that were used as mood induction procedures (MIPs) for this population. In addition, the VEs’ efficacy at increasing joy and relaxation in elderly users is analyzed. The VEs contain exercises for generating positive-autobiographic memories, mindfulness and slow breathing rhythms. The total sample comprised 18 participants over 55 years old who used the VEs on two occasions. Twelve of them used the joy environment, while 16 used the relaxation environment. Moods before and after each session were assessed using Visual Analogical Scales. After using both VEs, results indicated significant increases in joy and relaxation and significant decreases in sadness and anxiety. The participants also indicated low levels of difficulty of use and high levels of satisfaction and sense of presence. Hence, the VEs demonstrate their usefulness at promoting positive affects and enhancing the wellbeing of elderly people. Abstract Copyright © 2012 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption

Abstract Social network services (SNS) focus on building online communities of people who share interests and/or activities, or who are interested in exploring the interests and activities of others. This study examines security, trust, and privacy concerns with regard to social networking Websites among consumers using both reliable scales and measures. It proposes an SNS acceptance model by integrating cognitive as well as affective attitudes as primary influencing factors, which are driven by underlying beliefs, perceived security, perceived privacy, trust, attitude, and intention. Results from a survey of SNS users validate that the proposed theoretical model explains and predicts user acceptance of SNS substantially well. The model shows excellent measurement properties and establishes perceived privacy and perceived security of SNS as distinct constructs. The finding also reveals that perceived security moderates the effect of perceived privacy on trust. Based on the results of this study, practical implications for marketing strategies in SNS markets and theoretical implications are recommended accordingly. Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: Usability testing: what have we overlooked?

Abstract For more than a decade, the number of usability test participants has been a major theme of debate among usability practitioners and researchers keen to improve usability test performance. This paper provides evidence suggesting that the focus be shifted to task coverage instead. Our data analysis of nine commercial usability test teams participating in the CUE-4 study revealed no significant correlation between the percentage of problems found or of new problems and number of test users, but correlations of both variables and number of user tasks used by each usability team were significant. The role of participant recruitment on usability test performance and future research directions are discussed. Abstract Copyright © 2013 ACM, Inc.

Title: Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior

Abstract This paper tests the ability of two consumer theories—the theory of reasoned action and the theory of planned behavior—in predicting consumer online grocery buying intention. In addition, a comparison of the two theories is conducted. Data were collected from two web-based surveys of Danish ( n =1222) and Swedish ( n =1038) consumers using self-administered questionnaires. These results suggest that the theory of planned behavior (with the inclusion of a path from subjective norm to attitude) provides the best fit to the data and explains the highest proportion of variation in online grocery buying intention. Abstract Copyright © 2013 Elsevier B.V. All rights reserved.

Title: Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions

Abstract The Theory of Planned Behavior, an extension of the well-known Theory of Reasoned Action, is proposed as a model to predict consumer adoption intention. Three variations of the Theory of Planned Behavior are examined and compared to the Theory of Reasoned Action. The appropriateness of each model is assessed with data from a consumer setting. Structural equation modelling using maximum likelihood estimation for the four models revealed that the traditional forms of the Theory of Reasoned Action and the Theory of Planned Behavior fit the data adequately. Decomposing the belief structures and allowing for crossover effects in the Theory of Planned Behavior resulted in improvements in model prediction. The application of each model to theory development and management intervention is explored. Abstract Copyright © 1995 Elsevier B.V. All rights reserved.

Title: Knowledge and the Prediction of Behavior: The Role of Information Accuracy in the Theory of Planned Behavior

Abstract The results of the present research question the common assumption that being well informed is a prerequisite for effective action to produce desired outcomes. In Study 1 ( N = 79), environmental knowledge had no effect on energy conservation, and in Study 2 ( N = 79), alcohol knowledge was unrelated to drinking behavior. Such disappointing correlations may result from an inappropriate focus on accuracy of information at the expense of its relevance to and support for the behavior. Study 3 ( N = 85) obtained a positive correlation between knowledge and pro-Muslim behavior, but Study 4 ( N = 89) confirmed the proposition that this correlation arose because responses on the knowledge test reflected underlying attitudes. Study 4 also showed that the correlation could become positive or negative by appropriate selection of questions for the knowledge test. The theory of planned behavior (Ajzen, 1991 ), with its focus on specific actions, predicted intentions and behavior in all four studies. Abstract Copyright © 2013 Informa plc

Link: h ttp://www.businessinsider.com/ron-johnson-apple-store-j-c-penney-2011-11

People come to the Apple Store for the experience — and they’re willing to pay a premium for that. There are lots of components to that experience, but maybe the most important — and this is something that can translate to any retailer — is that the staff isn’t focused on selling stuff, it’s focused on building relationships and trying to make people’s lives better. Abstract Copyright © 2013 Business Insider, Inc. All rights reserved.

Title : Naturalizing aesthetics: Brain areas for aesthetic appraisal across sensory modalities

Abstract We present here the most comprehensive analysis to date of neuroaesthetic processing by reporting the results of voxel-based meta-analyses of 93 neuroimaging studies of positive-valence aesthetic appraisal across four sensory modalities. The results demonstrate that the most concordant area of activation across all four modalities is the right anterior insula, an area typically associated with visceral perception, especially of negative valence (disgust, pain, etc.). We argue that aesthetic processing is, at its core, the appraisal of the valence of perceived objects. This appraisal is in no way limited to artworks but is instead applicable to all types of perceived objects. Therefore, one way to naturalize aesthetics is to argue that such a system evolved first for the appraisal of objects of survival advantage, such as food sources, and was later co-opted in humans for the experience of artworks for the satisfaction of social needs. Abstract Copyright © 2011 Elsevier Inc. All rights reserved.

Link: http://www.scientificamerican.com/article.cfm?id=the-neuroscience-of-beauty

Studies from neuroscience and evolutionary biology challenge this separation of art from non-art. Human neuroimaging studies have convincingly shown that the brain areas involved in aesthetic responses to artworks overlap with those that mediate the appraisal of objects of evolutionary importance, such as the desirability of foods or the attractiveness of potential mates. Hence, it is unlikely that there are brain systems specific to the appreciation of artworks; instead there are general aesthetic systems that determine how appealing an object is, be that a piece of cake or a piece of music. Abstract © 2013 Scientific American, a Division of Nature America, Inc.

Link: http://blogs.scientificamerican.com/symbiartic/2011/10/03/need-proof-that-were-visual-beings/

Link: http://hbr.org/web/slideshows/five-charts-that-changed-business/1-slide

Once in a while, a chart so deftly captures an important strategic insight that it becomes an iconic part of management thinking and a tool that shows up in MBA classrooms and corporate boardrooms for years to come. As HBR prepares for its 90th anniversary, in 2012, their editors have combed the magazine archives and other sources to select five charts that changed the shape of strategy. Abstract Copyright © 2013 Harvard Business School Publishing. All rights reserved.

Link: http://www.strategy-business.com/article/04412

It is a widely accepted and rarely challenged tenet of marketing that companies can sustain competitive advantage only through “new and improved” product differentiation based on unique features and benefits. What a mistake. By paying attention to what consumers really want, companies can attract new customers and create a distinctive brand. Abstract © 2013 Booz & Company Inc. All rights reserved.

Link: http://www.economist.com/node/17723028

If you can have everything in 57 varieties, making decisions becomes hard work. Many of these options have improved life immeasurably in the rich world, and to a lesser extent in poorer parts. They are testimony to human ingenuity and innovation. Free choice is the basis on which markets work, driving competition and generating economic growth. It is the cornerstone of liberal democracy. The 20th century bears the scars of too many failed experiments in which people had no choice. But amid all the dizzying possibilities, a nagging question lurks: is so much extra choice unambiguously a good thing? Abstract Copyright © The Economist Newspaper Limited 2013. All rights reserved.

Link: http://e.businessinsider.com/public/1099804

Mobile apps are becoming more important to people, not less important, according to this chart plucked from a big presentation on the internet. It’s an interesting trend because it shows how mobile behavior is different than traditional desktop computing behavior when it comes to the web. Abstract Copyright © 2013 Business Insider, Inc. All rights reserved.

Link: http://blogs.scientificamerican.com/scicurious-brain/2012/07/30/you-want-that-well-i-want-it-too-the-neuroscience-of-mimetic-desire/

Mimetic desire is more than jealously wanting something because someone else has it. Rather, it’s about valuing something because someone else values it . And it’s pretty easy to transmit the value. Just writing about Person A’s activities and habits and showing it to Person B will make Person B start to think Person A must have seen something good about the Toyota Camry…maybe his next car…

Link: http://www.united-academics.org/magazine/27212/visual-memory-blindness/

A well-known pheonomenon in psychology has been the ‘inattentional blindness’ principle. In fact, you might know it from experience: it means that people tend to fail seeing things in their visible fields when they have to focus on a task. Until now, it was thought that in order to cause the effect, a cluttered visual field is required. Recent research shows that the effect is present though in many more situations. Abstract Copyright United Academics 2012 Coypright – All rights Reserved

Link: http://www.businessinsider.com/18-24-texting-2011-9

Chart of the Day: According to the Pew Internet project , people in the 18-24 year-old range are sending and receiving 110 texts per day on average. The median number of texts sent/received by that group is 50 per day. Abstract Copyright © 2013 Business Insider, Inc. All rights reserved.

Link: http://www.businessinsider.com/chart-of-the-day-facebook-time-2011-9

Chart of the Day: A new report on social media from Nielsen shows U.S. users spent 53.5 billion minutes on Facebook in May, which is more time than was spent on the next four biggest sites. Abstract Copyright © 2013 Business Insider, Inc. All rights reserved.

Link: http://www.scientificamerican.com/article.cfm?id=your-brain-on-facebook

A recent study showed that certain brain areas expand in people who have greater numbers of friends on Facebook . There was a problem, though. The study, in Proceedings of the Royal Society B , was unable to resolve the question of whether “friending” plumps up the brain areas or whether people with a type of robustness in brain physiology are just natural social butterflies. But with the help of a few monkeys in England, teenagers everywhere may now have more ammunition to use against parents. Abstract © 2013 Scientific American, a Division of Nature America, Inc.

Link: http://iwc.oxfordjournals.org/content/26/3/196.abstract.html?etoc

Although advances in technology now enable people to communicate ‘anytime, anyplace’, it is not clear how citizens can be motivated to actually do so. This paper evaluates the impact of three principles of psychological empowerment, namely perceived self-efficacy, sense of community and causal importance, on public transport passengers’ motivation to report issues and complaints while on the move. A week-long study with 65 participants revealed that self-efficacy and causal importance increased participation in short bursts and increased perceptions of service quality over longer periods. Finally, we discuss the implications of these findings for citizen participation projects and reflect on design opportunities for mobile technologies that motivate citizen participation. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/208.abstract.html?etoc

This review paper argues that users of personal information management systems have three particularly pressing requirements, for which current systems do not fully cater: (i) To combat information overload, as the volume of information increases. (ii) To ease context switching, in particular, for users who face frequent interrupts in their work. (iii) To be supported in information integration, across a variety of applications. To meet these requirements, four broad technological approaches should be adopted in an incremental fashion: (i) The deployment of a unified file system to manage all information objects, including files, emails and webpage URLs. (ii) The use of tags to categorize information; implemented in a way which is backward-compatible with existing hierarchical file systems. (iii) The use of context to aid information retrieval; built upon existing file and tagging systems rather than creating a parallel context management system. (iv) The deployment of semantic technologies, coupled with the harvesting of all useful metadata. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/238.abstract.html?etoc

Projective techniques are used in psychology and consumer research to provide information about individuals’ motivations, thoughts and feelings. This paper reviews the use of projective techniques in marketing research and user experience (UX) research and discusses their potential role in understanding users, their needs and values, and evaluating UX in practical product development contexts. A projective technique called sentence completion is evaluated through three case studies. Sentence completion produces qualitative data about users’ views in a structured form. The results are less time-consuming to analyze than interview results. Compared with quantitative methods such as AttrakDiff, the results are more time consuming to analyze, but more information is retrieved on negative feelings. The results show that sentence completion is useful in understanding users’ perceptions and that the technique can be used to complement other methods. Sentence completion can also be used online to reach wider user groups. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/256.abstract.html?etoc

Cognitive load (CL) is experienced during critical tasks and also while engaged emotional states are induced either by the task itself or by extraneous experiences. Emotions irrelevant to the working memory representation may interfere with the processing of relevant tasks and can influence task performance and behavior, making the accurate detection of CL from nonverbal information challenging. This paper investigates automatic CL detection from facial features, physiology and task performance under affective interference. Data were collected from participants (n=20) solving mental arithmetic tasks with emotional stimuli in the background, and a combined classifier was used for detecting CL levels. Results indicate that the face modality for CL detection was more accurate under affective interference, whereas physiology and task performance were more accurate without the affective interference. Multimodal fusion improved detection accuracies, but it was less accurate under affective interferences. More specifically, the accuracy decreased with an increasing intensity of emotional arousal. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/269.abstract.html?etoc

In the field of virtual reality (VR), many efforts have been made to analyze presence, the sense of being in the virtual world. However, it is only recently that functional magnetic resonance imaging (fMRI) has been used to study presence during an automatic navigation through a virtual environment. In the present work, our aim was to use fMRI to study the sense of presence during a VR-free navigation task, in comparison with visualization of photographs and videos (automatic navigations through the same environment). The main goal was to analyze the usefulness of fMRI for this purpose, evaluating whether, in this context, the interaction between the subject and the environment is performed naturally, hiding the role of technology in the experience. We monitored 14 right-handed healthy females aged between 19 and 25 years. Frontal, parietal and occipital regions showed their involvement during free virtual navigation. Moreover, activation in the dorsolateral prefrontal cortex was also shown to be negatively correlated to sense of presence and the postcentral parietal cortex and insula showed a parametric increased activation according to the condition-related sense of presence, which suggests that stimulus attention and self-awareness processes related to the insula may be linked to the sense of presence. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/285.abstract.html?etoc

Unlike visual stimuli, little attention has been paid to auditory stimuli in terms of emotion prediction with physiological signals. This paper aimed to investigate whether auditory stimuli can be used as an effective elicitor as visual stimuli for emotion prediction using physiological channels. For this purpose, a well-controlled experiment was designed, in which standardized visual and auditory stimuli were systematically selected and presented to participants to induce various emotions spontaneously in a laboratory setting. Numerous physiological signals, including facial electromyogram, electroencephalography, skin conductivity and respiration data, were recorded when participants were exposed to the stimulus presentation. Two data mining methods, namely decision rules and k-nearest neighbor based on the rough set technique, were applied to construct emotion prediction models based on the features extracted from the physiological data. Experimental results demonstrated that auditory stimuli were as effective as visual stimuli in eliciting emotions in terms of systematic physiological reactivity. This was evidenced by the best prediction accuracy quantified by the F1 measure (visual: 76.2% vs. auditory: 76.1%) among six emotion categories (excited, happy, neutral, sad, fearful and disgusted). Furthermore, we also constructed culture-specific (Chinese vs. Indian) prediction models. The results showed that model prediction accuracy was not significantly different between culture-specific models. Finally, the implications of affective auditory stimuli in human–computer interaction, limitations of the study and suggestions for further research are discussed. Abstract 2013 Oxford University Press.

Link: http://www.sciencedirect.com/science/article/pii/S0160289614000087

The deliberate practice view has generated a great deal of scientific and popular interest in expert performance. At the same time, empirical evidence now indicates that deliberate practice, while certainly important, is not as important as Ericsson and colleagues have argued it is. In particular, we (Hambrick, Oswald, Altmann, Meinz, Gobet, & Campitelli, 2014) found that individual differences in accumulated amount of deliberate practice accounted for about one-third of the reliable variance in performance in chess and music, leaving the majority of the reliable variance unexplained and potentially explainable by other factors. Ericsson’s (2014) defense of the deliberate practice view, though vigorous, is undercut by contradictions, oversights, and errors in his arguments and criticisms, several of which we describe here. We reiterate that the task now is to develop and rigorously test falsifiable theories of expert performance that take into account as many potentially relevant constructs as possible. Abstract © 2014 Elsevier Inc.

Link: http://techcrunch.com/2013/02/05/amazon-to-launch-virtual-currency-amazon-coins-in-its-appstore-in-may/

Link: http://onlinelibrary.wiley.com/doi/10.1002/smj.2284/abstract

Link: http://iwc.oxfordjournals.org/content/early/2014/05/09/iwc.iwu016.abstract.html?papetoc

Wizard of Oz (WOZ) is a well-established method for simulating the functionality and user experience of future systems. Using a human wizard to mimic certain operations of a potential system is particularly useful in situations where extensive engineering effort would otherwise be needed to explore the design possibilities offered by such operations. The WOZ method has been widely used in connection with speech and language technologies, but advances in sensor technology and pattern recognition as well as new application areas such as human–robot interaction have made it increasingly relevant to the design of a wider range of interactive systems. In such cases, achieving acceptable performance at the user interface level often hinges on resource-intensive improvements such as domain tuning, which are better done once the overall design is relatively stable. Although WOZ is recognized as a valuable prototyping technique, surprisingly little effort has been put into exploring it from a methodological point of view. Starting from a survey of the literature, this paper presents a systematic investigation and analysis of the design space for WOZ for language technology applications, and proposes a generic architecture for tool support that supports the integration of components for speech recognition and synthesis as well as for machine translation. This architecture is instantiated in WebWOZ—a new web-based open-source WOZ prototyping platform. The viability of generic support is explored empirically through a series of evaluations. Researchers from a variety of backgrounds were able to create experiments, independent of their previous experience with WOZ. The approach was further validated through a number of real experiments, which also helped to identify a number of possibilities for additional support, and flagged potential issues relating to consistency in wizard performance. Abstract 2014 Oxford University Press

Link: http://www.thinkwithgoogle.com/insights/library/studies/the-new-multi-screen-world-study/

This paper studies how business models can be designed to tap effectively into open innovation labor markets with heterogeneously motivated workers. Using data on open source software, we show that motivations are diverse, and demonstrate how managers can strategically influence the flow of code contributions and their impact on project performance. Unlike previous literature using survey data, we exploit the observed pattern of project membership and code contributions—the “revealed preference” of developers—to infer the motivations driving their decision to contribute. Developers strongly sort along key dimensions of the business model chosen by project managers, especially the degree of openness of the project license. The results indicate an important role for intrinsic motivation, reputation, and labor market signaling, and a more limited role for reciprocity. Abstract 2014 John Wiley & Sons, Ltd.

updated on 5/13

Title: Developing elements of user experience for mobile phones and services: survey, interview, and observation approaches

Abstract The term user experience (UX) encompasses the concepts of usability and affective engineering. However, UX has not been defined clearly. In this study, a literature survey, user interview and indirect observation were conducted to develop definitions of UX and its elements. A literature survey investigated 127 articles that were considered to be helpful to define the concept of UX. An in-depth interview targeted 14 hands-on workers in the Korean mobile phone industry. An indirect observation captured daily experiences of eight end-users with mobile phones. This study collected various views on UX from academia, industry, and end-users using these three approaches. As a result, this article proposes definitions of UX and its elements: usability, affect, and user value. These results are expected to help design products or services with greater levels of UX. Abstract Copyright 2011 Wiley Periodicals, Inc.

Title: Why different people prefer different systems for different tasks: An activity perspective on technology adoption in a dynamic user environment

Abstract In a contemporary user environment, there are often multiple information systems available for a certain type of task. Based on the premises of Activity Theory, this study examines how user characteristics, system experiences, and task situations influence an individual’s preferences among different systems in terms of user readiness to interact with each. It hypothesizes that system experiences directly shape specific user readiness at the within-subject level, user characteristics and task situations make differences in general user readiness at the between-subject level, and task situations also affect specific user readiness through the mediation of system experiences. An empirical study was conducted, and the results supported the hypothesized relationships. The findings provide insights on how to enhance technology adoption by tailoring system development and management to various task contexts and different user groups. Abstract Copyright 2011 ASIS&T

Title: A review of factors influencing user satisfaction in information retrieval

Abstract The authors investigate factors influencing user satisfaction in information retrieval. It is evident from this study that user satisfaction is a subjective variable, which can be influenced by several factors such as system effectiveness, user effectiveness, user effort, and user characteristics and expectations. Therefore, information retrieval evaluators should consider all these factors in obtaining user satisfaction and in using it as a criterion of system effectiveness. Previous studies have conflicting conclusions on the relationship between user satisfaction and system effectiveness; this study has substantiated these findings and supports using user satisfaction as a criterion of system effectiveness. Abstract Copyright 2010 ASIS&T

Title: The development and evaluation of a survey to measure user engagement

Abstract Facilitating engaging user experiences is essential in the design of interactive systems. To accomplish this, it is necessary to understand the composition of this construct and how to evaluate it. Building on previous work that posited a theory of engagement and identified a core set of attributes that operationalized this construct, we constructed and evaluated a multidimensional scale to measure user engagement. In this paper we describe the development of the scale, as well as two large-scale studies (N=440 and N=802) that were undertaken to assess its reliability and validity in online shopping environments. In the first we used Reliability Analysis and Exploratory Factor Analysis to identify six attributes of engagement: Perceived Usability, Aesthetics, Focused Attention, Felt Involvement, Novelty, and Endurability. In the second we tested the validity of and relationships among those attributes using Structural Equation Modeling. The result of this research is a multidimensional scale that may be used to test the engagement of software applications. In addition, findings indicate that attributes of engagement are highly intertwined, a complex interplay of user-system interaction variables. Notably, Perceived Usability played a mediating role in the relationship between Endurability and Novelty, Aesthetics, Felt Involvement, and Focused Attention. Abstract Copyright 2009 ASIS&T

Title: Exploring user engagement in online news interactions

Abstract This paper describes a qualitative study of online news reading and browsing. Thirty people participated in a quasi-experimental study in which they were asked to browse a news website and select three stories to discuss at a social gathering. Semi-structured interviews were conducted post-task to understand participants’ perceptions of what makes online news reading and browsing engaging or non-engaging. Findings as presented within the experience-based framework of user engagement and demonstrate the complexity of users’ interactions with information content and systems in online news environments. This study extends the model of user engagement and contributes new insights into user’s experience in casual-leisure settings, such as online news, which has implications for other information domains. Abstract Copyright 2011 by American Society for Information Science and Technology

Abstract This chapter of The Fabric of Mobile Services: Software Paradigms and Business Demands contains sections titled: New Services and User Experience, User-Centered Simplicity and Experience, Methodologies for Simplicity and User Experience, and Case Studies: Simplifying Paradigms Abstract Copyright 2009 John Wiley & Sons, Inc.

Title: The Right Angle: Visual Portrayal of Products Affects Observers’ Impressions of Owners

Abstract Consumer products have long been known to influence observers’ impressions of product owners. The angle at which products are visually portrayed in advertisements, however, may be an overlooked factor in these effects. We hypothesize and find that portrayals of the same product from different viewpoints can prime different associations that color impressions of product and owner in parallel ways. In Study 1, automobiles were rated higher on status- and power-related traits (e.g., dominant , powerful ) when portrayed head-on versus in side profile, an effect found for sport utility vehicles (SUVs)—a category with a reputation for dominance—but not sedans. In Study 2, these portrayal-based associations influenced the impressions formed about the product’s owner: a target person was rated higher on status- and power-related traits when his SUV was portrayed head-on versus in side profile. These results suggest that the influence of visual portrayal extends beyond general evaluations of products to affect more specific impressions of products and owners alike, and highlight that primed traits are likely to influence impressions when compatible with other knowledge about the target. Abstract Copyright 2012 Wiley Periodicals, Inc

Title: The Counterfeit Self: The Deceptive Costs of Faking It

Abstract Although people buy counterfeit products to signal positive traits, we show that wearing counterfeit products makes individuals feel less authentic and increases their likelihood of both behaving dishonestly and judging others as unethical. In four experiments, participants wore purportedly fake or authentically branded sunglasses. Those wearing fake sunglasses cheated more across multiple tasks than did participants wearing authentic sunglasses, both when they believed they had a preference for counterfeits (Experiment 1a) and when they were randomly assigned to wear them (Experiment 1b). Experiment 2 shows that the effects of wearing counterfeit sunglasses extend beyond the self, influencing judgments of other people’s unethical behavior. Experiment 3 demonstrates that the feelings of inauthenticity that wearing fake products engenders—what we term the counterfeit selfmediate the impact of counterfeits on unethical behavior. Finally, we show that people do not predict the impact of counterfeits on ethicality; thus, the costs of counterfeits are deceptive. Abstract Copyright 2010 Francesca Gino, Michael I. Norton, and Dan Ariely3

Link: http://iwc.oxfordjournals.org/content/26/5/389.full.html?etoc

Menus are a key mechanism for organizing different commands in graphical user interfaces. Nowadays low-cost devices that allow using different interaction techniques in remote interfaces have become widespread. Nevertheless, their corresponding menus are direct adaptations from traditional ones. As a consequence, they are inaccurate and slow, and also produce tiredness. In this paper, we design, implement and evaluate a menu selection technique for remote interfaces, the Body Menu. This technique permits whole-body interaction and is specifically designed to take advantage of the proprioception sense. The Body Menu attaches virtual menu items to different parts of the body and selects them when the users reach these zones with their hands. We use the Microsoft Kinect to implement this system. Additionally, we compared it with the most representative menus, studied the best number of body parts to be used and analyzed how children interact with it. Abstract © 2013 Oxford University Publishing.

Link: http://iwc.oxfordjournals.org/content/26/5/403.full.html?etoc

We present the evaluation of an interactive audio map system that enables blind and partially sighted users to explore and navigate city maps from the safety of their home using simulated 3D audio and synthetic speech alone. We begin with a review of existing literature in the areas of spatial knowledge and wayfinding, auditory displays and auditory map systems, before describing how this research builds on and differentiates itself from this body of work. One key requirement was the ability to quantify the effectiveness of the audio map, so we describe the design and implementation of the evaluation, which took the form of a game downloaded by participants to their own computers. The results demonstrate that participants (blind, partially sighted and sighted) have acquired detailed spatial knowledge and also that the availability of positional audio cues significantly improves wayfinding performance. Abstract © 2013 Oxford University Publishing.

Link: http://iwc.oxfordjournals.org/content/26/5/417.full.html?etoc

Delegation is the practice of sharing authority with another individual to enable them to complete a specific task as a proxy. Practices to permit delegation can range from formal to informal arrangements and can involve spontaneous yet finely balanced notions of trust between people. This paper argues that delegation is a ubiquitous yet an unsupported feature of socio-technical computer systems and that this lack of support illustrates a particular neglect to the everyday financial practices of the more vulnerable people in society. Our contribution is to provide a first exploration of the domain of person-to-person delegation in digital payments, a particularly pressing context. We first report qualitative data collected across several studies concerning banking practices of individuals over 80 years of age. We then use analytical techniques centred upon identification of stakeholders, their concerns and interactions, to characterize the delegation practices we observed. We propose a Concerns Matrix as a suitable representation to capture conflicts in the needs of individuals in such complex socio-technical systems, and finally propose a putative design response in the form of a Helper Card. Abstract © 2013 Oxford University Publishing..

Link: Why We Love Beautiful Things

Great design, the management expert Gary Hamel once said, is like Justice Potter Stewart’s famous definition of pornography — you know it when you see it. You want it, too: brain scan studies reveal that the sight of an attractive product can trigger the part of the motor cerebellum that governs hand movement. Instinctively, we reach out for attractive things; beauty literally moves us. © 2013 New York Times

Link: http://www.bris.ac.uk/news/2013/9478.html

A new study has analysed tens of thousands of articles available to readers of online news and created a model to find out ‘what makes people click’. The aim of the study was to model the reading preferences for the audiences of 14 online news outlets using machine learning techniques. The models, describing the appeal of an article to each audience, were developed by linear functions of word frequencies. The models compared articles that became “most popular” on a given day in a given outlet with articles that did not. The research dentified the most attractive keywords, as well as the least attractive ones, and explained the choices readers made. Abstract © 2013 University of Bristol.

Title: Pointing and Selecting with Facial Activity

Abstract The aim of this paper was to evaluate the use of three facial actions (i.e. frowning, raising the eyebrows, and smiling) in selecting objects on a computer screen when gaze was used for pointing. Dwell time is the most commonly used selection technique in gaze-based interaction, and thus, a dwell time of 400 ms was used as a reference selection technique. A wireless, head-mounted prototype device that carried out eye tracking and contactless, capacitive measurement of facial actions was used for the interaction task. Participants (N=16) performed point-and-select tasks with three pointing distances (i.e. 60, 120 and 240 mm) and three target sizes (i.e. 25, 30 and 40 mm). Task completion times, pointing errors and throughput values based on Fitts’ law were used to compare the selection techniques. The participants also rated the techniques with subjective ratings scales. The results showed that the different techniques performed equally well in many respects. However, throughput values varied from 8.38 bits/s (raising the eyebrows) to 15.33 bits/s (smiling) and were comparable to or, in the case of smiling, better than in earlier research with similar interaction techniques. The dwell time was found to be the least accurate selection technique in terms of the magnitudes of point-and-select errors. Smiling technique was rated as more accurate to use than the frowning or the raising techniques. The results give further support for methods that combine facial behavior to eye tracking when interacting with technology.

Title: Modeling Traditional Literacy, Internet Skills and Internet Usage: An Empirical Study

Abstract This paper focuses on the relationships among traditional literacy (reading, writing and understanding text), medium-related Internet skills (consisting of operational and formal skills), content-related Internet skills (consisting of information and strategic skills) and Internet usage types (information- and career-directed Internet use and entertainment use). We conducted a large-scale survey that resulted in a dataset of 1008 respondents. The results reveal the following: (i) traditional literacy has a direct effect on formal and information Internet skills and an indirect effect on strategic Internet skills and (ii) differences in types of Internet usage are indirectly determined by traditional literacy and directly affected by Internet skills, such that higher levels of strategic Internet skills result in more information- and career-directed Internet use. Traditional literacy is a pre-condition for the employment of Internet skills, and Internet skills should not be considered an easy means of disrupting historically grounded inequalities caused by differences in traditional literacy.

Title: Life Is Too Short to RTFM: How Users Relate to Documentation and Excess Features in Consumer Products

Abstract This paper addresses two common problems that users of various products and interfaces encounter—over-featured interfaces and product documentation. Over-featured interfaces are seen as a problem as they can confuse and over-complicate everyday interactions. Researchers also often claim that users do not read product documentation, although they are often exhorted to ‘RTFM’ (read the field manual). We conducted two sets of studies with users which looked at the issues of both manuals and excess features with common domestic and personal products. The quantitative set was a series of questionnaires administered to 170 people over 7 years. The qualitative set consisted of two 6-month longitudinal studies based on diaries and interviews with a total of 15 participants. We found that manuals are not read by the majority of people, and most do not use all the features of the products that they own and use regularly. Men are more likely to do both than women, and younger people are less likely to use manuals than middle-aged and older ones. More educated people are also less likely to read manuals. Over-featuring and being forced to consult manuals also appears to cause negative emotional experiences. Implications of these findings are discussed.

Title: Effect of Age on Human–Computer Interface Control Via Neck Electromyography

Abstract The purpose of this study was to determine the effect of age on visuomotor tracking using submental and anterior neck surface electromyography (sEMG) to assess feasibility of computer control via neck musculature, which allows people with little remaining motor function to interact with computers. Thirty-two healthy adults participated: 16 younger adults aged 18–29 years and 16 older adults aged 69–85 years. Participants modulated sEMG to achieve targets presented at different amplitudes using real-time visual feedback. Root mean squared (RMS) error was used to quantify tracking performance. RMS error was increased for older adults relative to younger adults. Older adults demonstrated more RMS error than younger adults as a function of increasing target amplitude. The differential effects of age found on static tracking performance in anterior neck musculature suggest more difficult translation of human–computer interfaces controlled using anterior neck musculature for static tasks to older populations.

Title: Should I Stay or Should I Go? Improving Event Recommendation in the Social Web

Abstract This paper focuses on the recommendation of events in the Social Web, and addresses the problem of finding if, and to which extent, certain features, which are peculiar to events, are relevant in predicting the users’ interests and should thereby be taken into account in recommendation. We consider, in particular, three ‘additional’ features that are usually shown to users within social networking environments: reachability from the user location, the reputation of the event in the community and the participation of the user’s friends. Our study is aimed at evaluating whether adding this information to the description of the event type and topic, and including in the user profile the information on the relevance of these factors, can improve our capability to predict the user’s interest. We approached the problem by carrying out two surveys with users, who were asked to express their interest in a number of events. We then trained, by means of linear regression, a scoring function defined as a linear combination of the different factors, whose goal was to predict the user scores. We repeated this experiment under different hypotheses on the additional factors, in order to assess their relevance by comparing the predictive capabilities of the resulting functions. The compared results of our experiments show that additional factors, if properly weighted, can improve the prediction accuracy with an error reduction of 4.1%. The best results were obtained by combining content-based factors and additional factors in a proportion of ∼10:4.

Title: “I Need to Be Explicit: You’re Wrong”: Impact of Face Threats on Social Evaluations in Online Instructional Communication

Abstract Online instructional communication, as found in ask-an-expert forums, e-learning discussion boards or online help desks, creates situations that threaten the recipient’s face. This study analyzed the evaluation of face-threatening acts with a 1×3 design. An online forum thread confronted a layperson with an expert who either (a) addressed the layperson’s misconceptions directly and frankly, (b) mitigated face threats through explicit hints about the need to be direct or (c) communicated politely and indirectly. College students read these dialogues and assessed the expert communicator’s facework, recipient orientation, credibility and likability. Results showed that polite experts were evaluated most positively; explicit hints did not improve perceptions of face-threatening acts. This implies that users of instructional forums prefer communicators to be polite even when face threats are necessary. We discuss practical implications for different online instruction contexts and make suggestions for further research.

Title: The Potential of a Text-Based Interface as a Design Medium: An Experiment in a Computer Animation Environment

Abstract Since the birth of the concept of direct manipulation, the graphical user interface has been the dominant means of controlling digital objects. In this research, we hypothesize that the benefits of a text-based interface involve multiple tradeoffs, and we explore the potential of text as a medium of design from three perspectives: (i) the perceived level of control of the designed object, (ii) a tool for realizing creative ideas and (iii) an effective form for a highly learnable user interface. Our experiment in a computer animation environment shows that (i) participants did feel a high level of control of characters, (ii) creativity was both restricted and facilitated depending on the task and (iii) natural language expedited the learning of a new interface language. Our research provides experimental proof of the effect of a text-based interface and offers guidelines for the design of future computer-aided design applications.

Title: Framing a Set: Understanding the Curatorial Character of Personal Digital Bibliographies

Abstract We articulate a model of curatorship that emphasizes framing the character of the curated set as the focus of curatorial activity. This curatorial character is structured through the articulation, via mechanisms of selection, description and arrangement, of coherent classificatory principles. We describe the latest stage of a continuing project to examine the curatorial character of personal digital bibliographies, such as Pinterest boards, Flickr galleries and GoodReads shelves, and to support the design of such curatorially expressive personal collections. In the study reported here, 24 participants created personal bibliographies using either a structured design process, with explicit tasks for selecting, describing and arranging collection items, or an unstructured process that did not separate these activities. Our findings lead to a more complex understanding of personal collections as curatorial, expressive artifacts. We explore the role of cohesion as a quality that facilitates expression of the curatorial frame, and we find that when designers read source materials as a part of a set, they are more likely to write cohesive collections. Our findings also suggest that the curatorial act involves both the definition of abstract classificatory principles and their instantiation in a specific material environment. We describe various framing devices that facilitate these reading and writing activities, and we suggest design directions for supporting curatorial reading and writing tasks.

Title: Identifying Problems Associated with Focus and Context Awareness in 3D Modelling Tasks

Abstract Creating complex 3D models is a challenging process. One of the main reasons for this is that 3D models are usually created using software developed for conventional 2D displays which lack true depth perspective, and therefore do not support correct perception of spatial placement and depth-ordering of displayed content. As a result, modellers often have to deal with many overlapping components of 3D models (e.g. vertices, edges, faces, etc.) on a 2D display surface. This in turn causes them to have difficulties in distinguishing distances, maintaining position and orientation awareness, etc. To better understand the nature of these problems, which can collectively be defined as ‘focus and context awareness’ problems, we have conducted a pilot study with a group of novice 3D modellers, and a series of interviews with a group of professional 3D modellers. This article presents these two studies, and their findings, which have resulted in identifying a set of focus and context awareness problems that modellers face in creating 3D models using conventional modelling software. The article also provides a review of potential solutions to these problems in the related literature.

Title: Researching Young Children’s Everyday Uses of Technology in the Family Home

Abstract Studies of the everyday uses of technology in family homes have tended to overlook the role of children and, in particular, young children. A study that was framed by an ecocultural approach focusing on children’s play and learning with toys and technologies is used to illustrate some of the methodological challenges of conducting research with young children in the home. This theoretical framework enabled us to identify and develop a range of methods that illuminated the home’s unique mix of inhabitants, learning opportunities and resources and to investigate parents’ ethnotheories, or cultural beliefs, that gave rise to the complex of practices, values and attitudes and their intersections with technology and support for learning in the home. This resulted in a better understanding of the role of technology in the lives of these 3- and 4-year-old children.

Title: Measuring web usability using item response theory: Principles, features and opportunities

Abstract Usability is considered a critical issue on the web that determines either the success or the failure of a company. Thus, the evaluation of usability has gained substantial attention. However, most current tools for usability evaluation have some limitations, such as excessive generality and a lack of reliability and validity. The present work proposes the construction of a tool to measure usability in e-commerce websites using item response theory (IRT). While usability issues have only been considered in theoretical or empirical contexts, in this study, we discuss them from a mathematical point of view using IRT. In particular, we develop a standardised scale to measure usability in e-commerce websites. This study opens a new field of research in the ergonomics of interfaces with respect to the development of scales using IRT.

Title: Everything Science Knows Right Now About Standing Desks

Abstract If it wasn’t already clear through common sense, it’s become painfully clear through science that sitting all day is terrible for your health. What’s especially alarming about this evidence is that extra physical activity doesn’t seem to offset the costs of what researchers call “prolonged sedentary time.” Just as jogging and tomato juice don’t make up for a night of smoking and drinking, a little evening exercise doesn’t erase the physical damage done by a full work day at your desk.

In response some people have turned to active desks—be it a standing workspace or even a treadmill desk—but the research on this recent trend has been too scattered to draw clear conclusions on its benefits (and potential drawbacks). At least until now. A trio of Canada-based researchers has analyzed the strongest 23 active desk studies to draw some conclusions on how standing and treadmill desks impact both physiological health and psychological performance. Abstract Copyright 2015 Eric Jaffe

Send Us Your Research References: If you have interesting and relevant research references post, post content as comment below for possible inclusion in next year’s updated list.

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Angry Birds UX: Why Angry Birds is so successful and popular: a cognitive teardown of the user experience (1.5 million page views). https://live-mauro-usability-science.pantheonsite.io/blog/why-angry-birds-is-so-successful-a-cognitive-teardown-of-the-user-experience/

Apple v. Samsung: Impact and Implications for Product Design, User Interface Design (UX), Software Development and the Future of High-Technology Consumer Products https://live-mauro-usability-science.pantheonsite.io/blog/apple-v-samsung-implications-for-product-design-user-interface-ux-design-software-development-and-the-future-of-high-technology-consumer-products/

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Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

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Object name is PCR-9-184-g001.jpg

Classification of research study designs

Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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A Panorama of Design

A look at design-world events, products and people.

Spurts of water shooting out from an outdoor fountain. Ancient Roman columns are behind the fountain.

By The New York Times

This article is part of our Design special section about water as a source of creativity.

For Ancient Roman Baths, a Chance to Sparkle Again

After a 1,500-year dry spell, the Baths of Caracalla in Rome are being restored to their full aquatic splendor. Built in the early third century during the reigns of the emperor Septimius Severus and his son Caracalla, and inagurated in 216, they hosted as many as 8,000 visitors daily until the source of their water was destroyed by the Visigoths in the sixth century.

The renewal of the 25-acre site is a 10-year project overseen by Daniela Porro, the special superintendent of Rome, and Mirella Serlorenzi, the monument’s director. Last month, the first completed phase — the introduction of a 137-by-105-foot shallow reflecting pool known as the Water Mirror, designed by the architects Hannes Peer and Paolo Bornello — opened to the public.

In an email, Mr. Peer described being inspired by the Natatio, an Olympic-size pool in the ancient bath complex. The Water Mirror includes 20 submerged water jets fitted with reflectors that send up delicate, light-infused geysers. A stage that sits nearly flush with the pool’s surface is intended for theatrical performances, lectures and concerts.

Mr. Peer is also involved in redesigning the entrances to the monument, to bring back its connection to the urban fabric, and in adding a botanical garden, refreshment areas and other amenities. The buildings that surrounded the baths, whose walls, colonnades and large open spaces provided inspiration for the former Pennsylvania Station, among other architectural showstoppers, will be restored. “Caracalla,” he said, “is a very comprehensive and complex project.” rome.net/baths-caracalla . — ARLENE HIRST

Adding Zing to History

For their room in this year’s Kips Bay Decorator Show House, Ann Pyne, the president of the interior design firm McMillen, and Elizabeth Pyne Singer, a partner in the firm (and Ms. Pyne’s daughter), took their inspiration from Blair House, the presidential guest house across from the White House. They began with a reproduction of a storied chinoiserie wallpaper that was used in a 1964 restoration of the Lee Drawing Room.

But the women didn’t want to simply recreate the space, designed by Eleanor McMillen Brown, McMillen’s founder. They tweaked the 18th-century style by, for instance, commissioning a textured white-and-metallic mantle from the Brooklyn ceramic artist Peter Lane.

“It’s a matter of having challenging objects with the conservative idea of the wallpaper and Blair House,” Ms. Pyne said. The obvious mantle choice, she added, would have been from the Federal period.

Some of the colors used in the room, including the acid green in the 1950s Italian armchair, amp up the neutral palette favored by Jacqueline Kennedy, who supervised the restoration of Blair House in its early stages, when she was the first lady. According to John S. Botello, a designer who wrote his master’s thesis about Blair House, Mrs. Kennedy thought a suggestion by a decorator to use chartreuse, fuchsia and other zinging colors was inappropriate for a traditional house. Until May 28 at 125 East 65th Street; kipsbaydecoratorshowhouse.org . — STEPHEN TREFFINGER

A Flowery Revival

No flower, no matter how brilliantly hued or outlandishly petaled, much interests the Polish artist Marcin Rusak until it desiccates. Then the staff at his Warsaw studio embeds it in plastic and metal to form vessels and furniture with drooping and bulging contours, as if the vegetation were still trying to grow.

Through May 24, Carpenters Workshop Gallery in Midtown Manhattan is displaying 10 of his new works in an exhibition, “Vas Florum: Resina Botanica.” On his milky resin vases (priced from $20,000 to $30,000 each), petrified blooms and fronds overlap, as if cast aside by jilted brides or adrift in streams. Amoeba-shaped tables made of resin and bronze (from $90,000 to $120,000 each), with flowers strewn against rust and dark green backgrounds, resemble rocks full of fossils.

Mr. Rusak said that he is particularly interested in how the flower industry has manipulated plants to maximize marketability, whether breeding stems to reduce thorns or dyeing petals in fluorescent colors. When an artificially colored blossom undergoes his embedding processes, pigment streaks can burst from the petals, as if the plant is eagerly shedding its artificial disguise. “It’s a very weird scenario,” Mr. Rusak said; carpentersworkshopgallery.com. — EVE M. KAHN

Nickey Kehoe Sets Up Shop in the West Village

Relocating to New York can mean an endless real estate slog. For Todd Nickey and Amy Kehoe, the owners of the Los Angeles design studio and home boutique Nickey Kehoe, who were looking to set up their first Manhattan store, the process was remarkably frictionless.

“We were only beginning our search when we stumbled upon it,” said Ms. Kehoe about the mid-19th-century Italianate brownstone in the West Village where they are renting two floors. (The artist Jackson Pollock also resided there early in his career.)

The pair developed a large trade clientele over the years in the New York City area, so it made sense to have a presence there, they said. The shop offers the duo’s branded designs, vintage pieces, globally sourced items and works by other craftspeople — much as in Los Angeles. “We’ve always had an East Coast and a European sensibility,” Mr. Nickey said. Even their California emporium was inspired by 1990s hardware and design shops in New York, where they met.

The upper floor, called the Salon, is dedicated to furniture, lighting, textiles and bespoke objects. The Household section downstairs features more basic items for the pantry, laundry room and garden.

Nickey Kehoe is at 49 East 10th Street, between Broadway and University Place; nickeykehoe.com . — STEPHEN TREFFINGER

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Computer Science > Computation and Language

Title: qserve: w4a8kv4 quantization and system co-design for efficient llm serving.

Abstract: Quantization can accelerate large language model (LLM) inference. Going beyond INT8 quantization, the research community is actively exploring even lower precision, such as INT4. Nonetheless, state-of-the-art INT4 quantization techniques only accelerate low-batch, edge LLM inference, failing to deliver performance gains in large-batch, cloud-based LLM serving. We uncover a critical issue: existing INT4 quantization methods suffer from significant runtime overhead (20-90%) when dequantizing either weights or partial sums on GPUs. To address this challenge, we introduce QoQ, a W4A8KV4 quantization algorithm with 4-bit weight, 8-bit activation, and 4-bit KV cache. QoQ stands for quattuor-octo-quattuor, which represents 4-8-4 in Latin. QoQ is implemented by the QServe inference library that achieves measured speedup. The key insight driving QServe is that the efficiency of LLM serving on GPUs is critically influenced by operations on low-throughput CUDA cores. Building upon this insight, in QoQ algorithm, we introduce progressive quantization that can allow low dequantization overhead in W4A8 GEMM. Additionally, we develop SmoothAttention to effectively mitigate the accuracy degradation incurred by 4-bit KV quantization. In the QServe system, we perform compute-aware weight reordering and take advantage of register-level parallelism to reduce dequantization latency. We also make fused attention memory-bound, harnessing the performance gain brought by KV4 quantization. As a result, QServe improves the maximum achievable serving throughput of Llama-3-8B by 1.2x on A100, 1.4x on L40S; and Qwen1.5-72B by 2.4x on A100, 3.5x on L40S, compared to TensorRT-LLM. Remarkably, QServe on L40S GPU can achieve even higher throughput than TensorRT-LLM on A100. Thus, QServe effectively reduces the dollar cost of LLM serving by 3x. Code is available at this https URL .

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Evaluation of the Full-Frontal Crash Regulation for the M1 Category of Vehicles from an Indian Perspective

2024-01-2750.

1 What should be the appropriate test speed for the full-frontal test based on Indian accident data?
2 What is the suitable dummy configuration in terms of gender, seating position, and age to maximize occupant safety in full frontal accidents?
3 Is the proposed ATD’s anthropometry (weight and height) suitable, based on the people involved in full frontal cases in India?
4 What are occupant injury attributes in full-frontal accidents?

Trade Wars and the Optimal Design of Monetary Rules

Monetary rules may have a large effect on the outcome of trade wars if central banks target the CPI inflation rate or more generally changes in the relative price of traded goods. We lay out a two-country open-economy model with sticky prices where countries engage in trade wars. In the presence of monopoly pricing markups, we show that the final level of tariffs and welfare losses from trade wars critically depend on the design of monetary policy. If central banks adopt a fixed nominal exchange rate or even better target the CPI inflation rate, trade wars are much less intense than those under PPI inflation targeting. We further show that an optimally delegated monetary rule that internalizes the formation of non-cooperative trade policy can actually completely eliminate a trade war, and even act to partly offset the welfare cost of monopoly markups.

Devereux thanks SSHRC for research funding. Auray and Eyquem acknowledge the financial support of Projets Generique ANR 2015, Grant Number ANR-15-CE33-0001-01. Finally we acknowledge the financial support of the Europlace Institute of Finance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

The Centre for Business Taxation is an independent research centre of the University of Oxford, based at the Saïd Business School. The Centre receives financial support from a number of sources, including currently around 30 companies. A full list of current and past corporate donors to the Centre for Business Taxation, as well as a statement about the independence of the centre from its donors, is available at http://www.sbs.ox.ac.uk/ideas-impact/tax/about/funding.

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